We have around 500k keys maintained by one memcache server. Around 499k keys are stored in one Slab [it is always Slab #8].
The key names have this format: BarData:Currency[0099]YYYY-MM-DD_HH:MM:SS
Currency is one of 23 different expressions [$EURUSD, $GBPUSD, ...]
The [] hold a 4 digit number which alternates between 0001, 0003, 0005, 0010, 0015, 0030, 0060, 0090 and 0120
The datetime format is very similar due the fact the data is saved for ascending continuous date period.
Does this affect the performance when accessing the memcache keys and should we consider to change the key names in order to spread it over more Slabs or can we leave it how it is?
According to this answer https://stackoverflow.com/a/10139350 memcache stores items with equal size in the same Slab. In my case hashing the key name does not change the Slab because all items have the same size.
Related
I have over 800 data in AWS DynamoDB.
I configured id as partition key and its format is 4 digit number as String
( e.g 0001, 0050, 0800.)
But, I can't see what I expected.
How can I see them with alignment?
DynamoDB does not sort the items it returns to you by their partition key. If you want items to be sorted be field_x you need to define field_x as a sort key.
However, for the time being you are only going to store small amounts of data (800 items is certainly considered "small", or even "tiny", from DynamoDB's point of view) you can just do the sorting on your side.
I am working on a database that (hopefully) will end up using a primary key with both numbers and letters in the values to track lots of agricultural product. Due to the way in which the weighing of product takes place at more than one facility, I have no other option but to maintain the same base number but use letters in addition to this base number to denote split portions of each lot of product. The problem is, after I create record number 99, the number 100 suddenly floats up and underneath 10. This makes it difficult to maintain consistency and forces me to replace this alphanumeric lot ID with a strictly numeric value in order to keep it sorted (which I use "autonumber" as the data type). Either way, I need the alphanumeric lot ID, and so having 2 ID's for the same lot can be confusing for anyone inputting values into the form. Is there a way around this that I am just not seeing?
If you're using query as a data source then you may try to sort it by string converted to number, something like
SELECT id, field1, field2, ..
ORDER BY CLng(YourAlphaNumericField)
Edit: you may also try Val function instead of CLng - it should not fail on non-numeric input
Why not properly format your key before saving ? e.g: "0000099". You will avoid a costly conversion later.
Alternatively, you could use 2 fields as the composite PK. One with the Number (as Long) and one with the Location (as String).
I have a table with 100+ values corresponding to each row, so I'm exploring different ways to store them.
Without any indexes, would I lose anything if I store these 100 values in an integer[] column in postgresql? As compared to storing them in separate columns.
Plus, since we can add indexes to array elemnets,
CREATE INDEX test_index on test ((foo[1]));
Would there be a performance difference queries using such an index as compared to regular index on a column?
As far as I've read, this performance difference would come into picture in arrays with variable length elements; but I'm not sure about fixed length ones.
Don't go for the lazy way.
If you need to store 100 and more values as array, it is ok, if it has sense has array for your application, your data.
If you need to query for a specific element of the array, then this design is not good, regardless of performances, and you must use columns. This will help you in the moment you must delete a "column" in the middle or redesign it.
Anyway, as wrote by Frank in comments, if values are all same type, consider to model them to another table (if also the meaning is the same).
We have data with key-multipleValues. Each key can have around 500 values (each value will be around 200-300 chars) and the number of such keys will be around 10 million. Major operation is to check for a value given a key.
I've been using mysql for long time where i've got 2 options: one row for each keyvalue, one row for each key with all values in a text field.But these does not seem efficient to me as the first model has lot of rows,redundancies and second model text field will become very large .
I am considering using nosql database for this purpose, i've used mongodb before and i dont think it is suitable for my current case. keyvalue based or column family based nosql db would be better.It need not be distributed.Someone who used riak,redis,cassandra etc pls share your thoughts.
Thanks
From your description, it seems some sort of Key-value store will be better for you comparing relational DB.
The data itself seem to be a non-relational, why store in a relational storage? It seems valid to use something like Cassandra.
I think a typical data-structure for this data to store will be a column family, with Key as Row-key and Columns as value.
MyDATA: (ColumnFamily)
RowKey=>Key
Column1=>val1
Column2=>val2
...
...
ColumnN=valN
The data would look like (JSON notation):
MyDATA (CF){
[
{key1:[{val1-1:'', timestamp}, {val1-2:'', timestamp}, .., {val1-500:'', timestamp}]},
{key2:[{val2-1:'', timestamp}, {val2-2:'', timestamp}, .., {val2-500:'', timestamp}]},
...
...
]
}
Hopefully this helps.
Try the direct, normalized approach: One table with this schema:
id (primary key)
key
value
You have one row for every key->value relation
Add an index for each column, and lookup should be reasonably efficient. Have you profiled any of this to exhibit a bottleneck?
This does map straightforwardly to Cassandra. Row key will be your model key, and your model values will be column names (yes, names) in Cassandra. You can leave the Cassandra column value empty, or add metadata there such as timestamp if that would be useful.
I don't think this is beyond the scale of MySQL on a single machine. You'll need to tune inserts or it'll take forever to load. You might also consider compressing your values using COMPRESS() or in your app directly. Might save you 50% or so.
Redis is basically an in-memory database, so it's probably out. Riak might be a decent choice or HBase or Cassandra.
I have a Cassandra ColumnFamily (0.6.4) that will have new entries from users. I'd like to query Cassandra for those new entries so that I can process that data in another system.
My sense was that I could use a TimeUUIDType as the key for my entry, and then query on a KeyRange that starts either with "" as the startKey, or whatever the lastStartKey was. Is this the correct method?
How does get_range_slice actually create a range? Doesn't it have to know the data type of the key? There's no declaration of the data type of the key anywhere. In the storage_conf.xml file, you declare the type of the columns, but not of the keys. Is the key assumed to be of the same type as the columns? Or does it do some magic sniffing to guess?
I've also seen reference implementations where people store TimeUUIDType in columns. However, this seems to have scale issues as this particular key would then become "hot" since every change would have to update it.
Any pointers in this case would be appreciated.
When sorting data only the column-keys are important. The data stored is of no consequence neither is the auto-generated timestamp. The CompareWith attribute is important here. If you set CompareWith as UTF8Type then the keys will be interpreted as UTF8Types. If you set the CompareWith as TimeUUIDType then the keys are automatically interpreted as timestamps. You do not have to specify the data type. Look at the SlicePredicate and SliceRange definitions on this page http://wiki.apache.org/cassandra/API This is a good place to start. Also, you might find this article useful http://www.sodeso.nl/?p=80 In the third part or so he talks about slice ranging his queries and so on.
Doug,
Writing to a single column family can sometimes create a hot spot if you are using an Order-Preserving Partitioner, but not if you are using the default Random Partitioner (unless a subset of users create vastly more data than all other users!).
If you sorted your rows by time (using an Order-Preserving Partitioner) then you are probably even more likely to create hotspots, since you will be adding rows sequentially and a single node will be responsible for each range of the keyspace.
Columns and Keys can be of any type, since the row key is just the first column.
Virtually, the cluster is a circular hash key ring, and keys get hashed by the partitioner to get distributed around the cluster.
Beware of using dates as row keys however, since even the randomization of the default randompartitioner is limited and you could end up cluttering your data.
What's more, if that date is changing, you would have to delete the previous row since you can only do inserts in C*.
Here is what we know :
A slice range is a range of columns in a row with a start value and an end value, this is used mostly for wide rows as columns are ordered. Known column names defined in the CF are indexed however so they can be retrieved specifying names.
A key slice, is a key associated with the sliced column range as returned by Cassandra
The equivalent of a where clause uses secondary indexes, you may use inequality operators there, however there must be at least ONE equals clause in your statement (also see https://issues.apache.org/jira/browse/CASSANDRA-1599).
Using a key range is ineffective with a Random Partitionner as the MD5 hash of your key doesn't keep lexical ordering.
What you want to use is a Column Family based index using a Wide Row :
CompositeType(TimeUUID | UserID)
In order for this not to become hot, add a first meaningful key ("shard key") that would split the data accross nodes such as the user type or the region.
Having more data than necessary in Cassandra is not a problem, it's how it is designed, so what you must ask yourself is "what do I need to query" and then design a Column Family for it rather than trying to fit everything in one CF like you'd do in an RDBMS.